Free University of Berlin

About: Free University of Berlin is a education organization based out in Berlin, Germany. It is known for research contribution in the topics: Population & Context (language use). The organization has 35195 authors who have published 66525 publications receiving 2094403 citations. The organization is also known as: FU Berlin.

Cellulose as an Architectural Element in Spatially Structured Escherichia coli Biofilms

Abstract: Morphological form in multicellular aggregates emerges from the interplay of genetic constitution and environmental signals. Bacterial macrocolony biofilms, which form intricate three-dimensional structures, such as large and often radially oriented ridges, concentric rings, and elaborate wrinkles, provide a unique opportunity to understand this interplay of "nature and nurture" in morphogenesis at the molecular level. Macrocolony morphology depends on self-produced extracellular matrix components. In Escherichia coli, these are stationary phase-induced amyloid curli fibers and cellulose. While the widely used "domesticated" E. coli K-12 laboratory strains are unable to generate cellulose, we could restore cellulose production and macrocolony morphology of E. coli K-12 strain W3110 by "repairing" a single chromosomal SNP in the bcs operon. Using scanning electron and fluorescence microscopy, cellulose filaments, sheets and nanocomposites with curli fibers were localized in situ at cellular resolution within the physiologically two-layered macrocolony biofilms of this "de-domesticated" strain. As an architectural element, cellulose confers cohesion and elasticity, i.e., tissue-like properties that-together with the cell-encasing curli fiber network and geometrical constraints in a growing colony-explain the formation of long and high ridges and elaborate wrinkles of wild-type macrocolonies. In contrast, a biofilm matrix consisting of the curli fiber network only is brittle and breaks into a pattern of concentric dome-shaped rings separated by deep crevices. These studies now set the stage for clarifying how regulatory networks and in particular c-di-GMP signaling operate in the three-dimensional space of highly structured and "tissue-like" bacterial biofilms.

Instability and deformation of a spherical vesicle by pressure.

Abstract: The infinitesimal stability of a spherical vesicle (closed membrane) is studied as a function of the pressure difference between the outer and inner media. It is found that above some threshold pressure the spherical vesicle can be deformed into a shape associated with $l\mathrm{th}$-order spherical harmonics. The comparison with numerical examples calculated previously by Deuling and Helfrich shows good agreement. Some applications to red blood cells are discussed.

Gütekriterien qualitativer Forschung

Abstract: Der Ansatzpunkt und die Vorgabe fur diesen Beitrag sind Gutekriterien qualitativer Forschung in der Psychologie. In anderen Kontexten wird zwar mittlerweile ein breiterer Zugang zu der im Hintergrund virulenten Fragestellung gewahlt. So beschaftigt sich Seale (1999) explizit mit der Qualitat qualitativer Forschung, und diese wird auch im Fokus des Qualitatsmanagements in der Forschung weiterverfolgt (Flick 2008a). Im Kontext qualitativer Forschung in der Psychologie wird jedoch der Ansatzpunkt der Kriterien haufiger gewahlt (vgl. Steinke 1999, 2008). Dies und die nach wie vor im Raum stehende und auch von ausen an die qualitative Forschung herangetragene Frage nach Kriterien lassen es sinnvoll erscheinen, in diesem Beitrag die Problematik der Qualitat qualitativer Forschung unter der Uberschrift und mit dem Fokus „Kriterien“ zu behandeln.

Phylogenetic trait conservatism and the evolution of functional trade-offs in arbuscular mycorrhizal fungi.

Abstract: The diversity of functional and life-history traits of organisms depends on adaptation as well as the legacy of shared ancestry. Although the evolution of traits in macro-organisms is well studied, relatively little is known about character evolution in micro-organisms. Here, we surveyed an ancient and ecologically important group of microbial plant symbionts, the arbuscular mycorrhizal (AM) fungi, and tested hypotheses about the evolution of functional and life-history traits. Variation in the extent of root and soil colonization by AM fungi is constrained to a few nodes basal to the most diverse groups within the phylum, with relatively little variation associated with recent divergences. We found no evidence for a trade-off in biomass allocated to root versus soil colonization in three published glasshouse experiments; rather these traits were positively correlated. Partial support was observed for correlated evolution between fungal colonization strategies and functional benefits of the symbiosis to host plants. The evolution of increased soil colonization was positively correlated with total plant biomass and shoot phosphorus content. Although the effect of AM fungi on infection by root pathogens was phylogenetically conserved, there was no evidence for correlated evolution between the extent of AM fungal root colonization and pathogen infection. Variability in colonization strategies evolved early in the diversification of AM fungi, and we propose that these strategies were influenced by functional interactions with host plants, resulting in an evolutionary stasis resembling trait conservatism.

Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations

Abstract: There has been a recent surge of interest in using machine learning across chemical space in order to predict properties of molecules or design molecules and materials with the desired properties. Most of this work relies on defining clever feature representations, in which the chemical graph structure is encoded in a uniform way such that predictions across chemical space can be made. In this work, we propose to exploit the powerful ability of deep neural networks to learn a feature representation from low-level encodings of a huge corpus of chemical structures. Our model borrows ideas from neural machine translation: it translates between two semantically equivalent but syntactically different representations of molecular structures, compressing the meaningful information both representations have in common in a low-dimensional representation vector. Once the model is trained, this representation can be extracted for any new molecule and utilized as a descriptor. In fair benchmarks with respect to various human-engineered molecular fingerprints and graph-convolution models, our method shows competitive performance in modelling quantitative structure–activity relationships in all analysed datasets. Additionally, we show that our descriptor significantly outperforms all baseline molecular fingerprints in two ligand-based virtual screening tasks. Overall, our descriptors show the most consistent performances in all experiments. The continuity of the descriptor space and the existence of the decoder that permits deducing a chemical structure from an embedding vector allow for exploration of the space and open up new opportunities for compound optimization and idea generation.